GET DAILY SIGNALSCan A.I. Predict Earthquakes?
Earthquakes are one of nature’s more unpredictable phenomena. Quakes can cause staggering levels of damage and trigger other natural disasters, like tsunamis. Compounding the effects of the initial quake (called a “mainshock”) are a series of aftershocks – smaller earthquakes that can heighten the existing problems in a quake’s aftermath.
Science has been able to establish laws dictating the magnitude and timing of aftershocks – Omori’s law, Båth's law, and the Gutenberg–Richter law are all accepted by the scientific community as accurate representations of aftershock behavior. But predicting the location of the next quake before it hits has thus far been out of science’s reach. Now, Harvard and Google have leveraged artificial intelligence to predict the location of aftershocks with more accuracy than ever before – and up to a year after the mainshock of an earthquake.
The parties, which consisted of Harvard Department of Earth and Planetary Sciences post-doctoral fellow Phoebe DeVries and Google AI recruiting lead Brendan Meade, as well as additional Google machine learning researchers Martin Wattenberg and Fernanda Viégas, began their analysis by compiling information from 118 “major” earthquakes worldwide. Next, they applied a deep learning technique called a neural net – which teach a computer by analyzing pre-labeled examples from a database to establish patterns corresponding to each label – to that data.
This method enabled researchers to “analyze the relationships between static stress changes caused by the mainshocks and aftershock locations” in a way far more accurate than the pre-existing model (called the Coulomb failure stress change system). Using “a scale accuracy running from 0 to 1 – in which 1 is a perfectly accurate model and 0.5 is as good as flipping a coin”, the new system achieved a 0.849 to the Coulomb system’s 0.583.
The research generated an “unintended consequence” beyond the previously unseen level of accuracy – the ability “to identify physical quantities that may be important in earthquake generation”, creating potential new ways of understanding how earthquakes behave. This piece of the deep learning model is called the von Mises yield criterion – popular “in fields like metallurgy”, it calculates “when materials will begin to break under stress”, and now may have use in earthquake science that was discounted before.
Machine learning may be useful for dredging up previously-ignored insight from existing data, but the system remains imperfect. It is currently too slow to make real-time predictions, and its focus on static (rather than dynamic) stress means it does not present the full scope of potential earthquake prediction. But its improvement over its predecessor is a promising step forward for seismologists and AI researchers alike – with refinement, it could signal a new day in earthquake science.
If You’re Wondering When A.I. Will Start Making Market Predictions…
Guess what – it already is. Hedge funds and large institutional investors have been using Artificial Intelligence to analyze large data sets for investment opportunities, and they have also unleashed A.I. on charts to discover patterns and trends. Not only can the A.I. scan thousands of individual securities and cryptocurrencies for patterns and trends, and it generate trade ideas based on what it finds. Hedge funds have had a leg-up on the retail investor for some time now.
Not anymore. Tickeron has launched a new investment platform, and it is designed to give retail investors access to sophisticated AI for a multitude of functions:
- Finding stock patterns in the market
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- Making Predictions for price movements in the future, with “A.I. Rank” and level of confidence in the trade.
And much more. No longer is AI just confined to the biggest hedge funds in the world. It can now be accessed by everyday investors. Learn how on Tickeron.com.
Contributor
Sergey Savastiouk, Ph.D. has a degree in Applied Mathematics from Moscow University and has extensive experience as an entrepreneur, investor, manager, and mathematician. His professional expertise is in applied mathematics, mathematical modeling, system and pattern analysis, and software and hardware system integration. He has served as the CEO of several hi-tech start-up companies and nonprofit organizations, which has given him proven capabilities in business strategy for high-tech start-up companies, market assessment, company formation, team building, product development, marketing, and sales. He has published numerous articles in journals and magazines on related fields. As a retail investor, he spent 15 years developing his proprietary trading and quantitative algorithms (now Tickeron’s A.I.), which brought him significant returns in trading the stock market. His current work and goal in founding Tickeron is to bring professional, sophisticated stock market analysis capabilities to retail investors via an easy-to-use interface.
GOOGL sees its 50-day moving average cross bearishly below its 200-day moving average
The 50-day moving average for GOOGL moved below the 200-day moving average on October 29, 2026. This could be a long-term bearish signal for the stock as the stock shifts to an downward trend.
Technical Analysis (Indicators)
Bearish Trend Analysis
The Momentum Indicator moved below the 0 level on October 16, 2026. You may want to consider selling the stock, shorting the stock, or exploring put options on GOOGL as a result. In 42 of 79 cases where the Momentum Indicator fell below 0, the stock fell further within the subsequent month. The odds of a continued downward trend are 53%.
The Moving Average Convergence Divergence Histogram (MACD) for GOOGL turned negative on October 16, 2026. This could be a sign that the stock is set to turn lower in the coming weeks. Traders may want to sell the stock or buy put options. Tickeron's A.I.dvisor looked at 48 similar instances when the indicator turned negative. In 24 of the 48 cases the stock turned lower in the days that followed. This puts the odds of success at 50%.
GOOGL moved below its 50-day moving average on October 16, 2026 date and that indicates a change from an upward trend to a downward trend.
The 10-day moving average for GOOGL crossed bearishly below the 50-day moving average on October 23, 2026. This indicates that the trend has shifted lower and could be considered a sell signal. In 9 of 15 past instances when the 10-day crossed below the 50-day, the stock continued to move higher over the following month. The odds of a continued downward trend are 60%.
Following a 3-day decline, the stock is projected to fall further. Considering past instances where GOOGL declined for three days, the price rose further in 50 of 62 cases within the following month. The odds of a continued downward trend are 62%.
The Aroon Indicator for GOOGL entered a downward trend on October 27, 2026. This could indicate a strong downward move is ahead for the stock. Traders may want to consider selling the stock or buying put options.
Bullish Trend Analysis
The RSI Indicator shows that the ticker has stayed in the oversold zone for 4 days. The price of this ticker is presumed to bounce back soon, since the longer the ticker stays in the oversold zone, the more promptly an Uptrend is expected.
The Stochastic Oscillator shows that the ticker has stayed in the oversold zone for 12 days. The price of this ticker is presumed to bounce back soon, since the longer the ticker stays in the oversold zone, the more promptly an upward trend is expected.
Following a +3.56% 3-day Advance, the price is estimated to grow further. Considering data from situations where GOOGL advanced for three days, in 228 of 337 cases, the price rose further within the following month. The odds of a continued upward trend are 68%.
GOOGL may jump back above the lower band and head toward the middle band. Traders may consider buying the stock or exploring call options.
Fundamental Analysis (Ratings)
The Tickeron SMR rating for this company is 21 (best 1 - 100 worst), indicating very strong sales and a profitable business model. SMR (Sales, Margin, Return on Equity) rating is based on comparative analysis of weighted Sales, Income Margin and Return on Equity values compared against S&P 500 index constituents. The weighted SMR value is a proprietary formula developed by Tickeron and represents an overall profitability measure for a stock.
The Tickeron Profit vs. Risk Rating rating for this company is 37 (best 1 - 100 worst), indicating well-balanced risk and returns. The average Profit vs. Risk Rating rating for the industry is 93, placing this stock slightly better than average.
The Tickeron Price Growth Rating for this company is 64 (best 1 - 100 worst), indicating steady price growth. GOOGL’s price grows at a higher rate over the last 12 months as compared to S&P 500 index constituents.
The Tickeron Valuation Rating of 69 (best 1 - 100 worst) indicates that the company is slightly overvalued in the industry. This rating compares market capitalization estimated by our proprietary formula with the current market capitalization. This rating is based on the following metrics, as compared to industry averages: P/B Ratio (5.677) is normal, around the industry mean (1.069). P/E Ratio (14.332) is within average values for comparable stocks, (-42.791). Projected Growth (PEG Ratio) (1.225) is also within normal values, averaging (17.274). Dividend Yield (0.003) settles around the average of (0.985) among similar stocks. P/S Ratio (9.416) is also within normal values, averaging (71.888).
The Tickeron PE Growth Rating for this company is 89 (best 1 - 100 worst), pointing to worse than average earnings growth. The PE Growth rating is based on a comparative analysis of stock PE ratio increase over the last 12 months compared against S&P 500 index constituents.
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a holding company with interests in software, health care, transportation and other technologies
Industry InternetSoftwareServices
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